29 citations · 181 across the 51 of their papers we have counts for
16 papers · 1 filter
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction
Gang Qiao, Kaidong Hu, Seonghyeon Moon +4
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection a…
NP-Match: When Neural Processes meet Semi-Supervised Learning
Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti +3
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this wor…
DAReN: A Collaborative Approach Towards Reasoning And Disentangling
Pritish Sahu, Kalliopi Basioti, Vladimir Pavlovic
Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM), critically depend on the ability to identify the visual concepts us…
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach
Minyoung Kim, Vladimir Pavlovic
Variational autoencoder (VAE) is a very successful generative model whose key element is the so called amortized inference network, which can perform test time inference using a si…
Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach
Minyoung Kim, Ricardo Guerrero, Vladimir Pavlovic
We deal with the problem of learning the underlying disentangled latent factors that are shared between the paired bi-modal data in cross-modal retrieval. Our assumption is that th…
Recursive Inference for Variational Autoencoders
Minyoung Kim, Vladimir Pavlovic
Inference networks of traditional Variational Autoencoders (VAEs) are typically amortized, resulting in relatively inaccurate posterior approximation compared to instance-wise vari…